Objective:
The overall objective is to analyze multiple, disparate -omics datasets that have been established in the current research project in order to discover specific nodes of interaction. This will take the work from the descriptive discovery stage to the hypothesis generation and testing phases. Thus we aim to develop predictive algorithms for interactions at the tissue, cellular, and subcellular levels that will define long-distance effects on metabolism, protein function, interaction, and structure. To search datasets that incorporate previously defined sites of protein post-translational modification (PTM; phosphorylation, acetylation, Met-oxidation, and o-glycosylation) and develop methods to predict the location of as yet unreported PTM based upon common features in primary sequence and structure.

Approach:
Scientifically, the approach is to employ extant methods for computational analysis of protein sequence/structure in order to predict sites of PTM, as well as to develop new algorithms for the same use. Additionally, strategies will be developed to apply the computational methods to study not sequence interactions but rather large-scale -omics interactions, to predict nodes of confluence, and to develop strategies for modifying these interactions.